Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
# data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7effd433bf28>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7effd423ca58>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    input_real = tf.placeholder(tf.float32, shape=(None, image_height, image_width, image_channels))
    input_z = tf.placeholder(tf.float32, shape=(None, z_dim))
    learning_rate = tf.placeholder(tf.float32, shape=())

    return input_real, input_z, learning_rate

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def leaky_relu(x, alpha=0.2, name='leaky_relu'):
    return tf.maximum(x, alpha * x, name=name)
In [7]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    kinit = tf.random_normal_initializer(stddev=0.02)
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, kernel_initializer=kinit, padding='same')
        relu1 = leaky_relu(x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, kernel_initializer=kinit, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = leaky_relu(bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, kernel_initializer=kinit, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = leaky_relu(bn3)
        # 4x4x256
        
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    kinit = tf.random_normal_initializer(stddev=0.02)
    
    with tf.variable_scope('generator', reuse=(not is_train)):
        x1 = tf.layers.dense(z, 7*7*256)
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = leaky_relu(x1)
        # 7x7x256
        
        x2 = tf.layers.conv2d_transpose(relu1, 128, 5, strides=2, kernel_initializer=kinit, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = leaky_relu(x2)
        # 14x14x128
        
        x3 = tf.layers.conv2d_transpose(relu2, 64, 5, strides=2, kernel_initializer=kinit, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = leaky_relu(x3)
        # 28x28x64
        
        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 3, strides=1, kernel_initializer=kinit, padding='same')
        # 28x28x3
        
        out = tf.tanh(logits)
    
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, is_train=True, alpha=alpha)
    d_real_out, d_real_logits = discriminator(input_real, alpha=alpha)
    d_fake_out, d_fake_logits = discriminator(g_model, reuse=True, alpha=alpha)
    
    smooth = 0.1
    real_labels = tf.ones_like(d_real_out) * (1 - smooth)
    fake_labels = tf.zeros_like(d_fake_out)
    g_labels    = tf.ones_like(d_fake_out)

    d_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits, labels=real_labels))
    d_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=fake_labels))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=g_labels))

    d_loss = d_real_loss + d_fake_loss
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    d_updates = [opt for opt in update_ops if opt.name.startswith('discriminator')]
    g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]

    with tf.control_dependencies(d_updates):
        d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)

    with tf.control_dependencies(g_updates):
        g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
            
    return d_opt, g_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every=10, show_every=100):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha=0.2)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_images *= 2.0
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z})

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i + 1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 64
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.0461... Generator Loss: 0.8685
Epoch 1/1... Discriminator Loss: 0.7521... Generator Loss: 1.3183
Epoch 1/1... Discriminator Loss: 1.7349... Generator Loss: 0.4487
Epoch 1/1... Discriminator Loss: 0.9799... Generator Loss: 1.4594
Epoch 1/1... Discriminator Loss: 1.1636... Generator Loss: 0.8441
Epoch 1/1... Discriminator Loss: 1.1481... Generator Loss: 0.9250
Epoch 1/1... Discriminator Loss: 1.5033... Generator Loss: 0.6340
Epoch 1/1... Discriminator Loss: 0.9999... Generator Loss: 1.2226
Epoch 1/1... Discriminator Loss: 1.9571... Generator Loss: 0.2832
Epoch 1/1... Discriminator Loss: 1.4753... Generator Loss: 0.4687
Epoch 1/1... Discriminator Loss: 1.2690... Generator Loss: 0.7494
Epoch 1/1... Discriminator Loss: 1.1292... Generator Loss: 0.8664
Epoch 1/1... Discriminator Loss: 1.4903... Generator Loss: 0.7419
Epoch 1/1... Discriminator Loss: 1.0092... Generator Loss: 1.0725
Epoch 1/1... Discriminator Loss: 1.1059... Generator Loss: 1.0590
Epoch 1/1... Discriminator Loss: 1.3574... Generator Loss: 0.7000
Epoch 1/1... Discriminator Loss: 1.5189... Generator Loss: 0.4941
Epoch 1/1... Discriminator Loss: 1.1753... Generator Loss: 1.4244
Epoch 1/1... Discriminator Loss: 1.0996... Generator Loss: 1.1050
Epoch 1/1... Discriminator Loss: 0.9564... Generator Loss: 1.0442
Epoch 1/1... Discriminator Loss: 1.0159... Generator Loss: 0.9831
Epoch 1/1... Discriminator Loss: 1.0189... Generator Loss: 0.9320
Epoch 1/1... Discriminator Loss: 1.2969... Generator Loss: 0.7848
Epoch 1/1... Discriminator Loss: 0.9717... Generator Loss: 1.1774
Epoch 1/1... Discriminator Loss: 1.1240... Generator Loss: 1.1031
Epoch 1/1... Discriminator Loss: 1.1170... Generator Loss: 0.7059
Epoch 1/1... Discriminator Loss: 1.6068... Generator Loss: 1.8277
Epoch 1/1... Discriminator Loss: 1.3382... Generator Loss: 0.6757
Epoch 1/1... Discriminator Loss: 1.0292... Generator Loss: 0.9410
Epoch 1/1... Discriminator Loss: 1.2957... Generator Loss: 0.6424
Epoch 1/1... Discriminator Loss: 2.2994... Generator Loss: 0.1888
Epoch 1/1... Discriminator Loss: 1.0553... Generator Loss: 1.0833
Epoch 1/1... Discriminator Loss: 1.5398... Generator Loss: 0.4873
Epoch 1/1... Discriminator Loss: 1.1199... Generator Loss: 0.9401
Epoch 1/1... Discriminator Loss: 1.1747... Generator Loss: 1.4251
Epoch 1/1... Discriminator Loss: 1.2398... Generator Loss: 0.7692
Epoch 1/1... Discriminator Loss: 1.3978... Generator Loss: 0.7794
Epoch 1/1... Discriminator Loss: 1.0379... Generator Loss: 1.2309
Epoch 1/1... Discriminator Loss: 1.3483... Generator Loss: 0.6520
Epoch 1/1... Discriminator Loss: 1.2934... Generator Loss: 1.0218
Epoch 1/1... Discriminator Loss: 1.2280... Generator Loss: 0.7739
Epoch 1/1... Discriminator Loss: 1.5185... Generator Loss: 0.6628
Epoch 1/1... Discriminator Loss: 1.4284... Generator Loss: 0.8352
Epoch 1/1... Discriminator Loss: 1.5385... Generator Loss: 0.5261
Epoch 1/1... Discriminator Loss: 1.1525... Generator Loss: 0.8924
Epoch 1/1... Discriminator Loss: 1.1157... Generator Loss: 1.6096
Epoch 1/1... Discriminator Loss: 1.1653... Generator Loss: 1.0145
Epoch 1/1... Discriminator Loss: 1.3332... Generator Loss: 0.7351
Epoch 1/1... Discriminator Loss: 1.3684... Generator Loss: 0.8407
Epoch 1/1... Discriminator Loss: 1.5276... Generator Loss: 0.5133
Epoch 1/1... Discriminator Loss: 1.4367... Generator Loss: 0.6840
Epoch 1/1... Discriminator Loss: 1.1463... Generator Loss: 0.9859
Epoch 1/1... Discriminator Loss: 1.1785... Generator Loss: 1.3073
Epoch 1/1... Discriminator Loss: 1.2831... Generator Loss: 1.0954
Epoch 1/1... Discriminator Loss: 1.5111... Generator Loss: 0.5726
Epoch 1/1... Discriminator Loss: 1.3918... Generator Loss: 0.7758
Epoch 1/1... Discriminator Loss: 1.5435... Generator Loss: 0.5826
Epoch 1/1... Discriminator Loss: 1.3098... Generator Loss: 0.8455
Epoch 1/1... Discriminator Loss: 1.6109... Generator Loss: 0.5975
Epoch 1/1... Discriminator Loss: 1.3332... Generator Loss: 0.6320
Epoch 1/1... Discriminator Loss: 1.1632... Generator Loss: 0.8932
Epoch 1/1... Discriminator Loss: 1.4333... Generator Loss: 0.8625
Epoch 1/1... Discriminator Loss: 1.4511... Generator Loss: 0.7647
Epoch 1/1... Discriminator Loss: 1.2903... Generator Loss: 0.7101
Epoch 1/1... Discriminator Loss: 1.2828... Generator Loss: 0.9404
Epoch 1/1... Discriminator Loss: 1.7195... Generator Loss: 0.5275
Epoch 1/1... Discriminator Loss: 1.0870... Generator Loss: 1.0903
Epoch 1/1... Discriminator Loss: 1.2293... Generator Loss: 0.9819
Epoch 1/1... Discriminator Loss: 1.3876... Generator Loss: 0.7986
Epoch 1/1... Discriminator Loss: 1.2464... Generator Loss: 0.9499
Epoch 1/1... Discriminator Loss: 1.1415... Generator Loss: 0.8094
Epoch 1/1... Discriminator Loss: 1.3127... Generator Loss: 1.0562
Epoch 1/1... Discriminator Loss: 1.1147... Generator Loss: 0.9439
Epoch 1/1... Discriminator Loss: 1.2946... Generator Loss: 0.7222
Epoch 1/1... Discriminator Loss: 1.2376... Generator Loss: 1.3308
Epoch 1/1... Discriminator Loss: 1.2187... Generator Loss: 0.8725
Epoch 1/1... Discriminator Loss: 1.3771... Generator Loss: 0.9038
Epoch 1/1... Discriminator Loss: 1.4084... Generator Loss: 0.6376
Epoch 1/1... Discriminator Loss: 1.0883... Generator Loss: 0.9236
Epoch 1/1... Discriminator Loss: 1.3559... Generator Loss: 0.6848
Epoch 1/1... Discriminator Loss: 1.2777... Generator Loss: 1.0734
Epoch 1/1... Discriminator Loss: 1.1628... Generator Loss: 0.8904
Epoch 1/1... Discriminator Loss: 1.2626... Generator Loss: 0.9617
Epoch 1/1... Discriminator Loss: 1.2341... Generator Loss: 0.9826
Epoch 1/1... Discriminator Loss: 1.3126... Generator Loss: 0.8205
Epoch 1/1... Discriminator Loss: 1.3775... Generator Loss: 0.9441
Epoch 1/1... Discriminator Loss: 1.2151... Generator Loss: 0.8848
Epoch 1/1... Discriminator Loss: 1.3900... Generator Loss: 0.7302
Epoch 1/1... Discriminator Loss: 1.1008... Generator Loss: 0.9882
Epoch 1/1... Discriminator Loss: 1.0267... Generator Loss: 1.2391
Epoch 1/1... Discriminator Loss: 1.3001... Generator Loss: 0.7518
Epoch 1/1... Discriminator Loss: 1.3504... Generator Loss: 0.6764
Epoch 1/1... Discriminator Loss: 1.3361... Generator Loss: 0.6080
Epoch 1/1... Discriminator Loss: 1.2174... Generator Loss: 1.1316
Epoch 1/1... Discriminator Loss: 1.1769... Generator Loss: 0.8894
Epoch 1/1... Discriminator Loss: 1.2048... Generator Loss: 0.9638
Epoch 1/1... Discriminator Loss: 1.0953... Generator Loss: 0.9339
Epoch 1/1... Discriminator Loss: 1.1888... Generator Loss: 0.8310
Epoch 1/1... Discriminator Loss: 1.1776... Generator Loss: 0.8699
Epoch 1/1... Discriminator Loss: 1.2616... Generator Loss: 0.7042
Epoch 1/1... Discriminator Loss: 1.2557... Generator Loss: 0.8150
Epoch 1/1... Discriminator Loss: 1.3752... Generator Loss: 0.5514
Epoch 1/1... Discriminator Loss: 1.3009... Generator Loss: 0.6332
Epoch 1/1... Discriminator Loss: 1.2830... Generator Loss: 0.8677
Epoch 1/1... Discriminator Loss: 1.1594... Generator Loss: 1.1724
Epoch 1/1... Discriminator Loss: 1.3071... Generator Loss: 1.0142
Epoch 1/1... Discriminator Loss: 1.2132... Generator Loss: 0.8495
Epoch 1/1... Discriminator Loss: 1.0952... Generator Loss: 0.9713
Epoch 1/1... Discriminator Loss: 1.1422... Generator Loss: 1.0296
Epoch 1/1... Discriminator Loss: 1.2102... Generator Loss: 1.0257
Epoch 1/1... Discriminator Loss: 1.2028... Generator Loss: 0.8762
Epoch 1/1... Discriminator Loss: 1.1136... Generator Loss: 1.0329
Epoch 1/1... Discriminator Loss: 1.1899... Generator Loss: 0.9668
Epoch 1/1... Discriminator Loss: 1.1464... Generator Loss: 0.8959
Epoch 1/1... Discriminator Loss: 0.9874... Generator Loss: 1.1699
Epoch 1/1... Discriminator Loss: 1.2744... Generator Loss: 0.8083
Epoch 1/1... Discriminator Loss: 1.0889... Generator Loss: 1.1441
Epoch 1/1... Discriminator Loss: 1.2119... Generator Loss: 0.9438
Epoch 1/1... Discriminator Loss: 1.4738... Generator Loss: 0.6682
Epoch 1/1... Discriminator Loss: 1.1087... Generator Loss: 0.9545
Epoch 1/1... Discriminator Loss: 1.2314... Generator Loss: 0.8317
Epoch 1/1... Discriminator Loss: 1.1122... Generator Loss: 0.9148
Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 0.8049
Epoch 1/1... Discriminator Loss: 1.1600... Generator Loss: 0.8172
Epoch 1/1... Discriminator Loss: 1.1402... Generator Loss: 1.0995
Epoch 1/1... Discriminator Loss: 1.2792... Generator Loss: 0.7408
Epoch 1/1... Discriminator Loss: 1.0958... Generator Loss: 0.7781
Epoch 1/1... Discriminator Loss: 1.2327... Generator Loss: 1.0566
Epoch 1/1... Discriminator Loss: 1.2026... Generator Loss: 0.9901
Epoch 1/1... Discriminator Loss: 1.0270... Generator Loss: 1.1435
Epoch 1/1... Discriminator Loss: 1.1467... Generator Loss: 1.1655
Epoch 1/1... Discriminator Loss: 1.1984... Generator Loss: 0.9559
Epoch 1/1... Discriminator Loss: 1.1806... Generator Loss: 0.9182
Epoch 1/1... Discriminator Loss: 1.0908... Generator Loss: 1.2055
Epoch 1/1... Discriminator Loss: 1.2109... Generator Loss: 0.8972
Epoch 1/1... Discriminator Loss: 1.3299... Generator Loss: 0.7265
Epoch 1/1... Discriminator Loss: 1.1244... Generator Loss: 0.8541
Epoch 1/1... Discriminator Loss: 1.2794... Generator Loss: 0.9857
Epoch 1/1... Discriminator Loss: 1.1253... Generator Loss: 0.8617
Epoch 1/1... Discriminator Loss: 1.3300... Generator Loss: 0.5625
Epoch 1/1... Discriminator Loss: 1.5067... Generator Loss: 0.6074
Epoch 1/1... Discriminator Loss: 1.0556... Generator Loss: 1.2107
Epoch 1/1... Discriminator Loss: 1.0457... Generator Loss: 1.1505
Epoch 1/1... Discriminator Loss: 1.1260... Generator Loss: 0.7975
Epoch 1/1... Discriminator Loss: 1.0933... Generator Loss: 0.9892
Epoch 1/1... Discriminator Loss: 1.2577... Generator Loss: 0.6993
Epoch 1/1... Discriminator Loss: 1.1845... Generator Loss: 0.9324
Epoch 1/1... Discriminator Loss: 1.2358... Generator Loss: 0.7352
Epoch 1/1... Discriminator Loss: 1.0748... Generator Loss: 0.8254
Epoch 1/1... Discriminator Loss: 1.1302... Generator Loss: 0.8410
Epoch 1/1... Discriminator Loss: 1.1469... Generator Loss: 0.9273
Epoch 1/1... Discriminator Loss: 1.3398... Generator Loss: 0.7149
Epoch 1/1... Discriminator Loss: 1.2570... Generator Loss: 0.7765
Epoch 1/1... Discriminator Loss: 1.3227... Generator Loss: 0.6030
Epoch 1/1... Discriminator Loss: 1.5654... Generator Loss: 0.4721
Epoch 1/1... Discriminator Loss: 1.1246... Generator Loss: 0.8795
Epoch 1/1... Discriminator Loss: 1.5452... Generator Loss: 0.5715
Epoch 1/1... Discriminator Loss: 1.1769... Generator Loss: 0.8042
Epoch 1/1... Discriminator Loss: 1.3210... Generator Loss: 0.8402
Epoch 1/1... Discriminator Loss: 1.1440... Generator Loss: 0.7870
Epoch 1/1... Discriminator Loss: 1.2977... Generator Loss: 0.7220
Epoch 1/1... Discriminator Loss: 1.3294... Generator Loss: 0.7100
Epoch 1/1... Discriminator Loss: 1.2204... Generator Loss: 0.6945
Epoch 1/1... Discriminator Loss: 1.2101... Generator Loss: 0.7218
Epoch 1/1... Discriminator Loss: 1.1129... Generator Loss: 0.8803
Epoch 1/1... Discriminator Loss: 0.9936... Generator Loss: 1.0868
Epoch 1/1... Discriminator Loss: 1.2278... Generator Loss: 0.7444
Epoch 1/1... Discriminator Loss: 1.2380... Generator Loss: 0.9492
Epoch 1/1... Discriminator Loss: 1.3107... Generator Loss: 0.6962
Epoch 1/1... Discriminator Loss: 1.2897... Generator Loss: 0.6649
Epoch 1/1... Discriminator Loss: 1.1877... Generator Loss: 0.7987
Epoch 1/1... Discriminator Loss: 1.2390... Generator Loss: 0.6783
Epoch 1/1... Discriminator Loss: 1.1553... Generator Loss: 0.8395
Epoch 1/1... Discriminator Loss: 1.3824... Generator Loss: 0.7096
Epoch 1/1... Discriminator Loss: 1.3582... Generator Loss: 0.6081
Epoch 1/1... Discriminator Loss: 1.3012... Generator Loss: 0.6682
Epoch 1/1... Discriminator Loss: 1.3365... Generator Loss: 0.6036
Epoch 1/1... Discriminator Loss: 1.2869... Generator Loss: 0.6157
Epoch 1/1... Discriminator Loss: 1.2952... Generator Loss: 0.8695
Epoch 1/1... Discriminator Loss: 1.1044... Generator Loss: 0.8353
Epoch 1/1... Discriminator Loss: 1.2383... Generator Loss: 0.8919
Epoch 1/1... Discriminator Loss: 1.4842... Generator Loss: 1.0315
Epoch 1/1... Discriminator Loss: 1.5140... Generator Loss: 0.5508
Epoch 1/1... Discriminator Loss: 1.2697... Generator Loss: 0.7892
Epoch 1/1... Discriminator Loss: 1.4905... Generator Loss: 0.6198
Epoch 1/1... Discriminator Loss: 1.3374... Generator Loss: 0.7057
Epoch 1/1... Discriminator Loss: 1.4663... Generator Loss: 0.6294
Epoch 1/1... Discriminator Loss: 1.2871... Generator Loss: 0.6972
Epoch 1/1... Discriminator Loss: 1.3025... Generator Loss: 0.7656
Epoch 1/1... Discriminator Loss: 1.2150... Generator Loss: 1.0919
Epoch 1/1... Discriminator Loss: 1.1423... Generator Loss: 0.9058
Epoch 1/1... Discriminator Loss: 1.1774... Generator Loss: 0.7402
Epoch 1/1... Discriminator Loss: 1.0726... Generator Loss: 0.9764
Epoch 1/1... Discriminator Loss: 1.4188... Generator Loss: 0.6220
Epoch 1/1... Discriminator Loss: 1.2067... Generator Loss: 0.7463
Epoch 1/1... Discriminator Loss: 1.3744... Generator Loss: 0.6402
Epoch 1/1... Discriminator Loss: 1.1184... Generator Loss: 1.2104
Epoch 1/1... Discriminator Loss: 1.1912... Generator Loss: 0.7733
Epoch 1/1... Discriminator Loss: 1.2030... Generator Loss: 0.8841
Epoch 1/1... Discriminator Loss: 1.2986... Generator Loss: 0.7206
Epoch 1/1... Discriminator Loss: 1.1554... Generator Loss: 0.7292
Epoch 1/1... Discriminator Loss: 1.1978... Generator Loss: 0.8510
Epoch 1/1... Discriminator Loss: 1.4691... Generator Loss: 0.5958
Epoch 1/1... Discriminator Loss: 1.1226... Generator Loss: 1.0501
Epoch 1/1... Discriminator Loss: 1.1165... Generator Loss: 0.8950
Epoch 1/1... Discriminator Loss: 1.2629... Generator Loss: 0.7199
Epoch 1/1... Discriminator Loss: 1.1344... Generator Loss: 1.1789
Epoch 1/1... Discriminator Loss: 1.2205... Generator Loss: 1.0184
Epoch 1/1... Discriminator Loss: 1.1800... Generator Loss: 0.7629
Epoch 1/1... Discriminator Loss: 1.2509... Generator Loss: 0.7739
Epoch 1/1... Discriminator Loss: 1.0324... Generator Loss: 0.9702
Epoch 1/1... Discriminator Loss: 1.1317... Generator Loss: 0.8501
Epoch 1/1... Discriminator Loss: 1.3463... Generator Loss: 1.0046
Epoch 1/1... Discriminator Loss: 1.1933... Generator Loss: 0.8303
Epoch 1/1... Discriminator Loss: 1.1245... Generator Loss: 0.8348
Epoch 1/1... Discriminator Loss: 1.1671... Generator Loss: 1.0041
Epoch 1/1... Discriminator Loss: 1.0065... Generator Loss: 0.9987
Epoch 1/1... Discriminator Loss: 1.3450... Generator Loss: 0.6844
Epoch 1/1... Discriminator Loss: 1.0627... Generator Loss: 1.0895
Epoch 1/1... Discriminator Loss: 1.1848... Generator Loss: 0.8557
Epoch 1/1... Discriminator Loss: 1.2320... Generator Loss: 0.9734
Epoch 1/1... Discriminator Loss: 1.2905... Generator Loss: 0.7761
Epoch 1/1... Discriminator Loss: 1.0940... Generator Loss: 0.9431
Epoch 1/1... Discriminator Loss: 1.1154... Generator Loss: 1.1482
Epoch 1/1... Discriminator Loss: 1.1727... Generator Loss: 0.9021
Epoch 1/1... Discriminator Loss: 1.0659... Generator Loss: 0.9954
Epoch 1/1... Discriminator Loss: 1.1233... Generator Loss: 0.9604
Epoch 1/1... Discriminator Loss: 1.0082... Generator Loss: 1.0786
Epoch 1/1... Discriminator Loss: 1.1343... Generator Loss: 1.1433
Epoch 1/1... Discriminator Loss: 1.3488... Generator Loss: 0.7138
Epoch 1/1... Discriminator Loss: 1.0886... Generator Loss: 1.0968
Epoch 1/1... Discriminator Loss: 1.3001... Generator Loss: 0.7805
Epoch 1/1... Discriminator Loss: 1.3647... Generator Loss: 0.6018
Epoch 1/1... Discriminator Loss: 1.2172... Generator Loss: 0.7592
Epoch 1/1... Discriminator Loss: 1.2326... Generator Loss: 0.7365
Epoch 1/1... Discriminator Loss: 1.2279... Generator Loss: 0.8090
Epoch 1/1... Discriminator Loss: 0.9186... Generator Loss: 1.0209
Epoch 1/1... Discriminator Loss: 1.1188... Generator Loss: 1.1689
Epoch 1/1... Discriminator Loss: 1.1637... Generator Loss: 0.9032
Epoch 1/1... Discriminator Loss: 1.2197... Generator Loss: 0.7348
Epoch 1/1... Discriminator Loss: 1.1315... Generator Loss: 0.7626
Epoch 1/1... Discriminator Loss: 1.1075... Generator Loss: 0.9816
Epoch 1/1... Discriminator Loss: 1.2931... Generator Loss: 0.6753
Epoch 1/1... Discriminator Loss: 1.2192... Generator Loss: 0.8041
Epoch 1/1... Discriminator Loss: 1.1885... Generator Loss: 1.0431
Epoch 1/1... Discriminator Loss: 1.3767... Generator Loss: 0.5622
Epoch 1/1... Discriminator Loss: 1.1586... Generator Loss: 0.9229
Epoch 1/1... Discriminator Loss: 1.2064... Generator Loss: 0.8475
Epoch 1/1... Discriminator Loss: 1.4966... Generator Loss: 0.6034
Epoch 1/1... Discriminator Loss: 1.0908... Generator Loss: 1.0397
Epoch 1/1... Discriminator Loss: 1.2063... Generator Loss: 0.7549
Epoch 1/1... Discriminator Loss: 1.2296... Generator Loss: 0.8524
Epoch 1/1... Discriminator Loss: 0.9715... Generator Loss: 1.1136
Epoch 1/1... Discriminator Loss: 1.1825... Generator Loss: 0.8358
Epoch 1/1... Discriminator Loss: 1.1942... Generator Loss: 0.7999
Epoch 1/1... Discriminator Loss: 1.1228... Generator Loss: 1.3347
Epoch 1/1... Discriminator Loss: 1.1971... Generator Loss: 0.7051
Epoch 1/1... Discriminator Loss: 1.2096... Generator Loss: 0.7798
Epoch 1/1... Discriminator Loss: 1.2228... Generator Loss: 0.7723
Epoch 1/1... Discriminator Loss: 1.4239... Generator Loss: 0.5493
Epoch 1/1... Discriminator Loss: 1.2229... Generator Loss: 1.2078
Epoch 1/1... Discriminator Loss: 1.0145... Generator Loss: 1.2207
Epoch 1/1... Discriminator Loss: 1.0959... Generator Loss: 0.9392
Epoch 1/1... Discriminator Loss: 1.2299... Generator Loss: 0.7250
Epoch 1/1... Discriminator Loss: 1.2625... Generator Loss: 0.7178
Epoch 1/1... Discriminator Loss: 1.1794... Generator Loss: 0.8595
Epoch 1/1... Discriminator Loss: 1.1862... Generator Loss: 0.7214
Epoch 1/1... Discriminator Loss: 1.1880... Generator Loss: 0.8469
Epoch 1/1... Discriminator Loss: 1.1941... Generator Loss: 0.8580
Epoch 1/1... Discriminator Loss: 1.1269... Generator Loss: 0.9782
Epoch 1/1... Discriminator Loss: 1.1537... Generator Loss: 0.8199
Epoch 1/1... Discriminator Loss: 1.1780... Generator Loss: 0.9490
Epoch 1/1... Discriminator Loss: 1.1178... Generator Loss: 1.1537
Epoch 1/1... Discriminator Loss: 0.8995... Generator Loss: 1.1675
Epoch 1/1... Discriminator Loss: 1.1129... Generator Loss: 0.9717
Epoch 1/1... Discriminator Loss: 0.9283... Generator Loss: 1.0913
Epoch 1/1... Discriminator Loss: 1.1374... Generator Loss: 0.8634
Epoch 1/1... Discriminator Loss: 1.1668... Generator Loss: 0.7911
Epoch 1/1... Discriminator Loss: 0.8481... Generator Loss: 1.2853
Epoch 1/1... Discriminator Loss: 1.2568... Generator Loss: 1.2852
Epoch 1/1... Discriminator Loss: 1.1984... Generator Loss: 0.7016
Epoch 1/1... Discriminator Loss: 1.1048... Generator Loss: 1.0277
Epoch 1/1... Discriminator Loss: 1.0569... Generator Loss: 1.1045
Epoch 1/1... Discriminator Loss: 1.1662... Generator Loss: 0.7838
Epoch 1/1... Discriminator Loss: 1.2280... Generator Loss: 0.8403
Epoch 1/1... Discriminator Loss: 1.1503... Generator Loss: 0.9852
Epoch 1/1... Discriminator Loss: 1.2048... Generator Loss: 0.7849
Epoch 1/1... Discriminator Loss: 1.2223... Generator Loss: 0.6817
Epoch 1/1... Discriminator Loss: 1.1990... Generator Loss: 0.8282
Epoch 1/1... Discriminator Loss: 1.3374... Generator Loss: 0.6711
Epoch 1/1... Discriminator Loss: 1.2097... Generator Loss: 0.8954
Epoch 1/1... Discriminator Loss: 1.3436... Generator Loss: 0.6752
Epoch 1/1... Discriminator Loss: 1.0661... Generator Loss: 1.0336
Epoch 1/1... Discriminator Loss: 1.0876... Generator Loss: 0.8700
Epoch 1/1... Discriminator Loss: 1.3599... Generator Loss: 0.7453
Epoch 1/1... Discriminator Loss: 1.1549... Generator Loss: 0.7670
Epoch 1/1... Discriminator Loss: 1.2079... Generator Loss: 0.7927
Epoch 1/1... Discriminator Loss: 1.0825... Generator Loss: 0.9682
Epoch 1/1... Discriminator Loss: 1.5167... Generator Loss: 0.6882
Epoch 1/1... Discriminator Loss: 1.3182... Generator Loss: 0.6705
Epoch 1/1... Discriminator Loss: 1.2574... Generator Loss: 0.8527
Epoch 1/1... Discriminator Loss: 1.2092... Generator Loss: 0.8538
Epoch 1/1... Discriminator Loss: 1.4708... Generator Loss: 0.5345
Epoch 1/1... Discriminator Loss: 1.4162... Generator Loss: 0.6303
Epoch 1/1... Discriminator Loss: 1.2896... Generator Loss: 0.9088
Epoch 1/1... Discriminator Loss: 1.1963... Generator Loss: 0.8466
Epoch 1/1... Discriminator Loss: 1.2694... Generator Loss: 0.8248
Epoch 1/1... Discriminator Loss: 1.2947... Generator Loss: 0.7479
Epoch 1/1... Discriminator Loss: 1.5042... Generator Loss: 0.5367
Epoch 1/1... Discriminator Loss: 1.1706... Generator Loss: 0.7901
Epoch 1/1... Discriminator Loss: 1.3114... Generator Loss: 0.7808
Epoch 1/1... Discriminator Loss: 1.1795... Generator Loss: 1.0492
Epoch 1/1... Discriminator Loss: 1.3319... Generator Loss: 0.6546
Epoch 1/1... Discriminator Loss: 1.1285... Generator Loss: 0.9250
Epoch 1/1... Discriminator Loss: 1.3030... Generator Loss: 0.8390
Epoch 1/1... Discriminator Loss: 1.1132... Generator Loss: 1.0075
Epoch 1/1... Discriminator Loss: 1.2744... Generator Loss: 0.8650
Epoch 1/1... Discriminator Loss: 1.2050... Generator Loss: 0.7746
Epoch 1/1... Discriminator Loss: 1.0439... Generator Loss: 1.2046
Epoch 1/1... Discriminator Loss: 1.2766... Generator Loss: 0.6583
Epoch 1/1... Discriminator Loss: 1.2771... Generator Loss: 0.8275
Epoch 1/1... Discriminator Loss: 1.2181... Generator Loss: 0.7724
Epoch 1/1... Discriminator Loss: 1.0553... Generator Loss: 0.9349
Epoch 1/1... Discriminator Loss: 1.4109... Generator Loss: 0.6937
Epoch 1/1... Discriminator Loss: 1.2689... Generator Loss: 0.9169
Epoch 1/1... Discriminator Loss: 1.0886... Generator Loss: 0.9692
Epoch 1/1... Discriminator Loss: 1.2595... Generator Loss: 0.6654
Epoch 1/1... Discriminator Loss: 1.2405... Generator Loss: 0.6135
Epoch 1/1... Discriminator Loss: 1.1214... Generator Loss: 0.8750
Epoch 1/1... Discriminator Loss: 1.1708... Generator Loss: 0.7584
Epoch 1/1... Discriminator Loss: 1.2249... Generator Loss: 0.9093
Epoch 1/1... Discriminator Loss: 1.1244... Generator Loss: 0.8808
Epoch 1/1... Discriminator Loss: 1.2347... Generator Loss: 0.9932
Epoch 1/1... Discriminator Loss: 1.1528... Generator Loss: 0.9197
Epoch 1/1... Discriminator Loss: 1.1618... Generator Loss: 0.9339
Epoch 1/1... Discriminator Loss: 1.2367... Generator Loss: 0.7253
Epoch 1/1... Discriminator Loss: 1.2384... Generator Loss: 0.8890
Epoch 1/1... Discriminator Loss: 1.3043... Generator Loss: 0.6226
Epoch 1/1... Discriminator Loss: 1.2091... Generator Loss: 0.7917
Epoch 1/1... Discriminator Loss: 1.4293... Generator Loss: 0.6913
Epoch 1/1... Discriminator Loss: 1.2023... Generator Loss: 0.7622
Epoch 1/1... Discriminator Loss: 1.1097... Generator Loss: 0.8496
Epoch 1/1... Discriminator Loss: 1.1757... Generator Loss: 0.9130
Epoch 1/1... Discriminator Loss: 1.0044... Generator Loss: 0.9392
Epoch 1/1... Discriminator Loss: 1.0472... Generator Loss: 1.0082
Epoch 1/1... Discriminator Loss: 1.3965... Generator Loss: 0.6493
Epoch 1/1... Discriminator Loss: 1.3502... Generator Loss: 0.6240
Epoch 1/1... Discriminator Loss: 1.1056... Generator Loss: 0.9336
Epoch 1/1... Discriminator Loss: 1.2058... Generator Loss: 1.0774
Epoch 1/1... Discriminator Loss: 1.0805... Generator Loss: 1.0914
Epoch 1/1... Discriminator Loss: 1.1753... Generator Loss: 0.7887
Epoch 1/1... Discriminator Loss: 1.3151... Generator Loss: 0.6152
Epoch 1/1... Discriminator Loss: 1.3384... Generator Loss: 0.8832
Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.5864
Epoch 1/1... Discriminator Loss: 1.3389... Generator Loss: 0.6863
Epoch 1/1... Discriminator Loss: 1.1293... Generator Loss: 1.0949
Epoch 1/1... Discriminator Loss: 1.2355... Generator Loss: 0.7922
Epoch 1/1... Discriminator Loss: 1.2755... Generator Loss: 0.8490
Epoch 1/1... Discriminator Loss: 1.1040... Generator Loss: 0.8761
Epoch 1/1... Discriminator Loss: 1.2836... Generator Loss: 0.6661
Epoch 1/1... Discriminator Loss: 1.4881... Generator Loss: 0.4743
Epoch 1/1... Discriminator Loss: 1.3670... Generator Loss: 0.7215
Epoch 1/1... Discriminator Loss: 1.3209... Generator Loss: 0.5787
Epoch 1/1... Discriminator Loss: 1.0424... Generator Loss: 0.9910
Epoch 1/1... Discriminator Loss: 1.0938... Generator Loss: 0.9826
Epoch 1/1... Discriminator Loss: 1.2206... Generator Loss: 0.8811
Epoch 1/1... Discriminator Loss: 1.2750... Generator Loss: 0.7545
Epoch 1/1... Discriminator Loss: 0.9578... Generator Loss: 1.0606
Epoch 1/1... Discriminator Loss: 1.1151... Generator Loss: 0.8848
Epoch 1/1... Discriminator Loss: 1.0727... Generator Loss: 1.0408
Epoch 1/1... Discriminator Loss: 1.1963... Generator Loss: 0.9607
Epoch 1/1... Discriminator Loss: 1.1097... Generator Loss: 1.2867
Epoch 1/1... Discriminator Loss: 1.1121... Generator Loss: 0.9340
Epoch 1/1... Discriminator Loss: 1.5407... Generator Loss: 0.4817
Epoch 1/1... Discriminator Loss: 1.1518... Generator Loss: 0.9052
Epoch 1/1... Discriminator Loss: 1.1809... Generator Loss: 0.7497
Epoch 1/1... Discriminator Loss: 1.2505... Generator Loss: 0.7691
Epoch 1/1... Discriminator Loss: 1.2662... Generator Loss: 0.7848
Epoch 1/1... Discriminator Loss: 1.1675... Generator Loss: 0.8154
Epoch 1/1... Discriminator Loss: 1.3214... Generator Loss: 0.7003
Epoch 1/1... Discriminator Loss: 1.1881... Generator Loss: 0.9076
Epoch 1/1... Discriminator Loss: 1.1762... Generator Loss: 0.8874
Epoch 1/1... Discriminator Loss: 1.2459... Generator Loss: 0.7934
Epoch 1/1... Discriminator Loss: 1.3812... Generator Loss: 0.6414
Epoch 1/1... Discriminator Loss: 1.2616... Generator Loss: 0.6783
Epoch 1/1... Discriminator Loss: 1.3650... Generator Loss: 0.7800
Epoch 1/1... Discriminator Loss: 1.2868... Generator Loss: 0.6964
Epoch 1/1... Discriminator Loss: 1.4539... Generator Loss: 0.7863
Epoch 1/1... Discriminator Loss: 1.0296... Generator Loss: 1.0362
Epoch 1/1... Discriminator Loss: 1.3087... Generator Loss: 0.6683
Epoch 1/1... Discriminator Loss: 1.1419... Generator Loss: 1.0376
Epoch 1/1... Discriminator Loss: 1.2223... Generator Loss: 0.7331
Epoch 1/1... Discriminator Loss: 1.2687... Generator Loss: 0.8486
Epoch 1/1... Discriminator Loss: 1.1666... Generator Loss: 0.8695
Epoch 1/1... Discriminator Loss: 1.1522... Generator Loss: 0.7043
Epoch 1/1... Discriminator Loss: 1.3628... Generator Loss: 0.6868
Epoch 1/1... Discriminator Loss: 1.3105... Generator Loss: 0.8955
Epoch 1/1... Discriminator Loss: 1.1947... Generator Loss: 0.7590
Epoch 1/1... Discriminator Loss: 1.2391... Generator Loss: 0.7480
Epoch 1/1... Discriminator Loss: 1.1429... Generator Loss: 0.7792
Epoch 1/1... Discriminator Loss: 1.1895... Generator Loss: 0.8210
Epoch 1/1... Discriminator Loss: 1.1761... Generator Loss: 0.7696
Epoch 1/1... Discriminator Loss: 1.2749... Generator Loss: 0.6471
Epoch 1/1... Discriminator Loss: 1.2778... Generator Loss: 0.8061
Epoch 1/1... Discriminator Loss: 1.1357... Generator Loss: 1.0205
Epoch 1/1... Discriminator Loss: 0.8698... Generator Loss: 1.2038
Epoch 1/1... Discriminator Loss: 1.3018... Generator Loss: 0.8305
Epoch 1/1... Discriminator Loss: 1.0873... Generator Loss: 1.0385
Epoch 1/1... Discriminator Loss: 1.1986... Generator Loss: 0.9566
Epoch 1/1... Discriminator Loss: 1.2746... Generator Loss: 0.6822
Epoch 1/1... Discriminator Loss: 1.3503... Generator Loss: 0.6250
Epoch 1/1... Discriminator Loss: 1.0548... Generator Loss: 1.0000
Epoch 1/1... Discriminator Loss: 1.0626... Generator Loss: 1.1569
Epoch 1/1... Discriminator Loss: 1.1536... Generator Loss: 1.0034
Epoch 1/1... Discriminator Loss: 1.3090... Generator Loss: 0.7121
Epoch 1/1... Discriminator Loss: 1.0740... Generator Loss: 0.9082
Epoch 1/1... Discriminator Loss: 1.3104... Generator Loss: 0.9309
Epoch 1/1... Discriminator Loss: 1.2380... Generator Loss: 0.6862
Epoch 1/1... Discriminator Loss: 1.2988... Generator Loss: 0.7653
Epoch 1/1... Discriminator Loss: 1.0986... Generator Loss: 0.9692
Epoch 1/1... Discriminator Loss: 1.4881... Generator Loss: 0.5221
Epoch 1/1... Discriminator Loss: 1.1850... Generator Loss: 0.8124
Epoch 1/1... Discriminator Loss: 1.1807... Generator Loss: 0.8249
Epoch 1/1... Discriminator Loss: 1.1207... Generator Loss: 1.1625
Epoch 1/1... Discriminator Loss: 1.1709... Generator Loss: 0.9327
Epoch 1/1... Discriminator Loss: 1.0981... Generator Loss: 1.0025
Epoch 1/1... Discriminator Loss: 1.0567... Generator Loss: 0.9994
Epoch 1/1... Discriminator Loss: 1.1440... Generator Loss: 0.8028
Epoch 1/1... Discriminator Loss: 1.1840... Generator Loss: 0.9007
Epoch 1/1... Discriminator Loss: 1.1224... Generator Loss: 0.8627
Epoch 1/1... Discriminator Loss: 1.2234... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 1.2115... Generator Loss: 0.7495
Epoch 1/1... Discriminator Loss: 1.0963... Generator Loss: 1.0416
Epoch 1/1... Discriminator Loss: 1.3976... Generator Loss: 0.6387

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.